Expertise

•  25 years studying the physiology of microalgae

•  15 years of dedicated developments

Our innovative approach is based on Charles Darwin's theory, which states that the application of selection pressure to a given population causes the individuals least adapted to this pressure to disappear.

In addition, the natural diversity of the population, combined with the rare and random mutations that constantly modify the organism's genome, will generate a few individuals that are more competitive in the face of the new pressure.

These more efficient individuals will pass on this modification to their descendants, until a new population appears, made up of individuals that are more productive or that overproduce a molecule of interest.

To do this, we are able to gradually increase the selection pressure differential between two extremes in the population. This dynamic selection approach, which is more difficult to control than simple selection by gradually increasing a selection pressure, considerably increases the effectiveness of the selection.

We make our selections using Selectiostats. This is a set of equipment that includes the reactor in the strict sense of the term, but also its entire data acquisition and system action environment for dynamically managing various signals.

This environment enables numerous selection pressures to be applied dynamically.



A great deal of work based on the theory of competition in a continuous reactor has been carried out to identify effective selection pressures and implement them in Selectiostats. The control algorithms are based on four key components:

1/ Models of the dynamic behaviour of microalgae, at different levels of complexity, in stress environments. These models include an acclimatisation component.

2/ Non-linear control strategies, based on mathematical models, to efficiently identify the individuals that optimise a function.

3/ Original sensors, capable of providing key information online about the state of the population (optical density, O2 content, pH, etc.)

4/ An online implementation strategy for these algorithms, using dedicated software that combines the use of data measured by the online sensors and offline data stored in a database to apply the algorithms developed.